Journal of Infrared and Millimeter Waves, Volume. 44, Issue 3, 431(2025)
Sparsity and self-similarity priors guided deep learning for blind image super-resolution
[1] Wang Z, Chen J, Hoi S C H. Deep learning for image super-resolution: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3365-3387(2021).
[2] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C](2017).
[3] Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution[C](2018).
[4] Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network[C](2018).
[5] Zhang Y, Li K, Li K et al. Image super-resolution using very deep residual channel attention networks[C](2018).
[6] Liu A, Liu Y, Gu J et al. Blind image super-resolution: a survey and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5461-5480(2023).
[7] Wang Q, Tang X, Shum H. Patch based blind image super resolution[C](2005).
[8] He Y, Yap K H, Chen L et al. A soft MAP framework for blind super-resolution image reconstruction[J]. Image and Vision Computing, 27, 364-373(2009).
[9] Bell-Kligler S, Shocher A, Irani M. Blind super-resolution kernel estimation using an internal-GAN[C](2019).
[10] Goodfellow I, Pouget-Abadie J, Mirza M et al. GAN (Generative Adversarial Nets)[J]. arXiv preprint(2014).
[11] Yamac M, Ataman B, Nawaz A. KernelNet: a blind super-resolution kernel estimation network[C](2021).
[12] Liang J, Zhang K, Gu S et al. Flow-based kernel prior with application to blind super-resolution[C](2021).
[13] Shocher A, Cohen N, Irani M. Zero-shot super-resolution using deep internal learning[C](2018).
[14] Zhang K, Zuo W, Zhang L. Learning a single convolutional super-resolution network for multiple degradations[C](2018).
[15] Zhang K, Van Gool L, Timofte. Deep unfolding network for image super-resolution[C](2020).
[16] Gu J, Lu H, Zuo W et al. Blind super-resolution with iterative kernel correction[C](2019).
[17] Jo Y, Oh S W, Vajda P et al. Tackling the Ill-posedness of super-resolution through adaptive target generation[C](2021).
[18] Kim S Y, Sim H, Kim M. KOALAnet: Blind super-resolution using kernel-oriented adaptive local adjustment[C](2021).
[19] Luo Z, Huang Y, Li S et al. Unfolding the alternating optimization for blind super resolution[C](2020).
[20] Luo Z, Huang Y, Li S et al. End-to-end alternating optimization for blind super resolution[J]. arXiv preprint(2021).
[21] Luo Z, Huang Y, Li S et al. End-to-end alternating optimization for real-world blind super resolution[J]. International Journal of Computer Vision, 131, 3152-3169(2023).
[22] Xu Z, Zhang Y, Luo T et al. Frequency principle: fourier analysis sheds light on deep neural networks[J]. arXiv preprint(2019).
[23] Luo Z, Huang H, Yu L et al. Deep constrained least squares for blind image super-resolution[C](2022).
[24] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2, 183-202(2009).
[25] Ali A, Tibshirani R J. The generalized lasso problem and uniqueness[J]. Electronic Journal of Statistics, 13, 2307-2347(2019).
[26] Son H, Lee S. Fast non-blind deconvolution via regularized residual networks with long/short skip-connections[C](2017).
[27] Dong J, Roth S, Schiele B. DWDN: Deep wiener deconvolution network for non-blind image deblurring[J]. IEEE Transactions on Pattern Analysis and Machine intelligence, 44, 9960-9976(2022).
[28] Guerquin-Kern M, Haberlin M, Pruessmann K P et al. A fast wavelet-based reconstruction method for magnetic resonance imaging[J]. IEEE Transactions on Medical Imaging, 30, 1649-1660(2011).
[29] Wang, Shuihua, Dong et al. Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging[J]. Information Sciences, 322, 115-132(2015).
[30] Zhang J, Ghanem B. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing[C](2018).
[31] Pan J, Sun D, Pfister H et al. Deblurring images via dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 2315-2328(2018).
Get Citation
Copy Citation Text
Sun-Yi GE, Xiao-Wei LUO, Shi-Yang FENG, Bin WANG. Sparsity and self-similarity priors guided deep learning for blind image super-resolution[J]. Journal of Infrared and Millimeter Waves, 2025, 44(3): 431
Category: Interdisciplinary Research on Infrared Science
Received: Oct. 22, 2024
Accepted: --
Published Online: Jul. 9, 2025
The Author Email: Bin WANG (wangbin@fudan.edu.cn)